Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan
As occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private mar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/2/172 |
| _version_ | 1797622271921094656 |
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| author | Peter H. F. Ng Peter Q. Chen Zackary P. T. Sin Sun H. S. Lai Andy S. K. Cheng |
| author_facet | Peter H. F. Ng Peter Q. Chen Zackary P. T. Sin Sun H. S. Lai Andy S. K. Cheng |
| author_sort | Peter H. F. Ng |
| collection | DOAJ |
| description | As occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private market occurred to meet the need for a more coordinated occupational rehabilitation practice. However, there is no clear service standard in private occupational rehabilitation services nor concrete suggestions on how to offer rehabilitation plans to injured workers. Electronic Health Records (EHRs) data can provide a foundation for developing a model to improve this situation. This project aims at using a machine-learning-based approach to enhance the traditional prediction of disability duration and rehabilitation plans for work-related injury and illness. To help patients and therapists to understand the machine learning result, we also developed an interactive dashboard to visualize machine learning results. The outcome is promising. Using the variational autoencoder, our system performed better in predicting disability duration. We have around 30% improvement compared with the human prediction error. We also proposed further development to construct a better system to manage the work injury case. |
| first_indexed | 2024-03-11T09:08:45Z |
| format | Article |
| id | doaj.art-38830d698598481d9a823ad1f7caf414 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T09:08:45Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-38830d698598481d9a823ad1f7caf4142023-11-16T19:10:38ZengMDPI AGBioengineering2306-53542023-01-0110217210.3390/bioengineering10020172Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation PlanPeter H. F. Ng0Peter Q. Chen1Zackary P. T. Sin2Sun H. S. Lai3Andy S. K. Cheng4Department of Rehabilitation Science, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Computing, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Computing, The Hong Kong Polytechnic University, Hong Kong, ChinaTotal Rehabilitation Management (HK) Limited, Hong Kong, ChinaDepartment of Rehabilitation Science, The Hong Kong Polytechnic University, Hong Kong, ChinaAs occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private market occurred to meet the need for a more coordinated occupational rehabilitation practice. However, there is no clear service standard in private occupational rehabilitation services nor concrete suggestions on how to offer rehabilitation plans to injured workers. Electronic Health Records (EHRs) data can provide a foundation for developing a model to improve this situation. This project aims at using a machine-learning-based approach to enhance the traditional prediction of disability duration and rehabilitation plans for work-related injury and illness. To help patients and therapists to understand the machine learning result, we also developed an interactive dashboard to visualize machine learning results. The outcome is promising. Using the variational autoencoder, our system performed better in predicting disability duration. We have around 30% improvement compared with the human prediction error. We also proposed further development to construct a better system to manage the work injury case.https://www.mdpi.com/2306-5354/10/2/172work injuryrehabilitation planrehabilitation case managementartificial intelligencevariational autoencoderinteractive dashboard |
| spellingShingle | Peter H. F. Ng Peter Q. Chen Zackary P. T. Sin Sun H. S. Lai Andy S. K. Cheng Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan Bioengineering work injury rehabilitation plan rehabilitation case management artificial intelligence variational autoencoder interactive dashboard |
| title | Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan |
| title_full | Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan |
| title_fullStr | Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan |
| title_full_unstemmed | Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan |
| title_short | Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan |
| title_sort | smart work injury management swim system a machine learning approach for the prediction of sick leave and rehabilitation plan |
| topic | work injury rehabilitation plan rehabilitation case management artificial intelligence variational autoencoder interactive dashboard |
| url | https://www.mdpi.com/2306-5354/10/2/172 |
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